Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tomoko Kitagawa
dblp:117/2205
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Haptics and multimodal interaction · 77% Learning and educational technologies · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Haptics and multimodal interaction › haptic feedback
haptic guidance |
0.1 | 1 | 2012 | A haptic instruction based assisted driving system for training the reverse parking · ICRA 2012 |
Learning and educational technologies › skill acquisition
driving skill training |
0.0 | 1 | 2012 | A haptic instruction based assisted driving system for training the reverse parking · ICRA 2012 |
Methods — techniques the papers use, named apart from their topics
haptic feedback · 0.1driving simulator · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | A haptic instruction based assisted driving system for training the reverse parkingabstractThe accident probability of beginner drivers is significantly higher than that of experienced drivers. It can be assumed that this is due to lack of driving skills which lead to making wrong decisions according to cognition and operating in correct way. In this paper, we propose a novel assisted driving system intended to help drivers to improve their skills for the reverse parking. The system is able to assist the driver by haptic instruction on the steering wheel in order to induce the driver to make the adequate operation. For the validation, we developed a 1/10 scale car simulator as a simulation environment on which we installed the proposed assistance method and conducted reverse parking experiment by using the simulator. According to the experiment, we validated that the parking accuracy and the trajectory similarity of subjects assisted by proposed system significantly increased compare to subjects unassisted. Consequently, the proposed assisted driving system could accelerate the learning of humans' driving skills. Masakazu Hirokawa, Naohisa Uesugi, Satoru Furugori, Tomoko Kitagawa, Kenji Suzuki 0002 |
ICRA | 4 |